code: def flip(x, dim):
      indices = [slice(None)] * x.dim()
      indices[dim] = torch.arange(
          x.size(dim) - 1, -1, -1, dtype=torch.long, device=x.device
      )
      return x[tuple(indices)]
  
file_path: insightface/reconstruction/ostec/external/graphonomy/FaceHairMask/graphonomy_inference.py
imports: import cv2
  import torch
  import timeit
  import numpy
  from PIL import Image
  import torch.nn.functional
  from torch.autograd import Variable
  from torchvision import transforms
  from FaceHairMask import graph
  from FaceHairMask import graphonomy_process
label_desc: Use image node_id 3 for calling a global function with example usage: flip(x, dim) and returns: x